Background

Our team delivers OpenPrescribing.net, a publicly funded and openly accessible explorer for NHS primary care prescribing supported by prescribing data openly published by the NHS Business Services Authority.

We were concerned that the NHS did not share hospital medicines data in a similar manner to primary care and that it should be shared (see Goldacre and MacKenna 2020).

In September 2020 NHS BSA published hospital medicines data for the first time. We have prepared the following notebook for investigating the use of COVID treatments in English hospitals.

If you have any feedback or insight The DataLab team can be contacted at .

Methods

Medicines codelist

The file med_covid_meds.csv contains the SNOMED codes for the medicines we are investigating. More information about each SNOMED code included in this analysis is shown in the methods section below (see dm+d Table).

# Define vector with selected SNOMED codes for COVID Treatments
codelist = read_csv(here("data/meds_covid_meds.csv"),
                    # convert to strings (they are stored as strings in SCMD table)
                    col_types = cols(id = col_character()))

# changed col type in read_csv function above 
# (read_csv works slightly different to read.csv)
codes = codelist$id

Data

Before we can analyse trends and variation in the use of asthma biologics we need to prepare our dataset. This analysis uses three different sources of data.

NHS Trusts

  • The NHS Digital “GP mapping file” which provides STP and region names mapped to STP and region ODS codes, published here.

  • The NHS Digital “Etr” file that maps trust organisation codes to trust names, STP ODS codes and region ODS codes, published here.

# Load ETR data
df_etr <- readr::read_csv(here::here("data/etr_tidy.csv")) %>%
  select(c("ods_code", "ods_name", "region_code", "stp_code")) 

# load older ETR data and find any additional codes here not in current list
df_etr_historic <- readr::read_csv(here::here("data/etr.csv"), 
                                   col_names = FALSE) %>%
  select(1:4) 

colnames(df_etr_historic) <- c("ods_code", "ods_name", "region_code", "stp_code")
 
df_etr_intersect <- intersect(df_etr$ods_code, df_etr_historic$ods_code)

df_etr_historic <- df_etr_historic %>% 
  dplyr::filter(!ods_code %in% df_etr_intersect)

df_etr <- rbind(df_etr, df_etr_historic)

# Load stp to regions data
stp_to_region_map <- read_csv(here::here("data/gp-reg-pat-prac-map.csv")) %>%
  group_by(STP_CODE, STP_NAME) %>%
  summarise(COMM_REGION_NAME = first(COMM_REGION_NAME),
            COMM_REGION_CODE = first(COMM_REGION_CODE)) %>%
  janitor::clean_names()

# check which STPs are in lookup table
stp_count <- df_etr %>% 
  group_by(stp_code) %>%
  summarise(n = n(),
            ods_code = first(ods_code),
            ods_name = first(ods_name))

stp_count <- left_join(stp_count, stp_to_region_map, by = "stp_code")
# Sustainability and Transformation Partnerships (STPs) 
df_etr %>% 
  left_join(stp_to_region_map, by = "stp_code") %>% 
  select(ods_name, stp_name, comm_region_name) %>% 
  mutate(stp_name = fct_explicit_na(stp_name),
         comm_region_name = fct_explicit_na(comm_region_name)) %>%
  reactable::reactable(filterable = TRUE,
                       columns = list(ods_name = reactable::colDef(name = "Name", 
                                                                   minWidth = 200),
                                      stp_name = reactable::colDef(name = "STP", 
                                                                   minWidth = 150),
                                      comm_region_name = reactable::colDef(name = "Region", 
                                                                           minWidth = 70)),
                       style = list(fontSize = "12px"),
                       highlight = TRUE)

SCMD

  • The Secondary Care Medicines Dataset (SCMD) published by the NHS BSA, see here.
# Secondary Care Medicines Data
# Connect to database, filter, and collect data.
# Get SCMD dataset
# Check negative quantities, seems to be a small (0.7%) problem in the data
# dplyr::tbl(conn_ebm_scmd, "scmd") %>% 
#   filter(between(year_month, "2019-01-01", "2019-12-31")) %>% 
#   # arrange(desc(year_month))
#   filter(total_quanity_in_vmp_unit > 0) %>% 
#   count()
#   group_by(vmp_snomed_code) %>% 
#   summarise(n = n(),
#             sum = sum(total_quanity_in_vmp_unit < 0))

db_scmd <- dplyr::tbl(conn_ebm_scmd, sql_query_scmd)

# Create dataframe for table
db_scmd <- db_scmd %>% 
  dplyr::filter(vmp_snomed_code %in% codes)

df_scmd <- dplyr::collect(db_scmd)

# df_scmd %>% skimr::skim()

# Tidy tidy tidy data
df_scmd_names <- df_scmd %>% 
  dplyr::left_join(dplyr::select(df_etr, ods_code, ods_name, stp_code), by = "ods_code") %>% 
  # some data cleaning as scmd uses some ods codes that are not up to date
  mutate(stp_code = as.character(stp_code),
         stp_code = case_when(
           ods_code == "RQ6" ~ "QYG", # cheshire + merseyside
           ods_code %in% c("RNL", "RE9", "RLN") ~ "QHM", # cumbria
           ods_code %in% c("RM2", "RW3") ~ "QOP", # Mcr
           ods_code == "RGQ" ~ "QJG", # Suffolk and North East Essex
           ods_code == "RJF" ~ "QJ2", # Derbyshire
           ods_code == "RR1" ~ "QHL", # Birmingham
           ods_code == "R1J" ~ "QR1", # gloucestershire (trust present in data but wrong/old code)
           ods_code == "R1E" ~ "QNC", # Staffs
           ods_code == "TAD" ~ "QWO", # W Yorks
           ods_code == "TAJ" ~ "QUA", # Black country
           ods_code == "TAH" ~ "QF7", # South Yorkshire & Bassetlow
           ods_code == "TAF" ~ "QMJ", # North Central London
           TRUE ~ stp_code
         ))

check_missing <- select(df_scmd_names,c("ods_code", "ods_name", "stp_code")) %>%
  distinct(.keep_all = TRUE)

check_missing <- check_missing[order(check_missing[["ods_name"]]), ]

# check which STPs are in data
scmd_stp_count <- df_scmd_names %>% 
  group_by(stp_code) %>%
  summarise(n = n(),
            ods_code = first(ods_code),
            ods_name = first(ods_name))
# Fill explicit missing and create dataset for sparkline in table
df_tab_sparkline <- df_scmd_names %>% 
  select(-c(vmp_product_name, ods_name, stp_code, stp_code, ods_name)) %>% 
  arrange(ods_code, vmp_snomed_code, year_month) %>% 
  as_tsibble(key = c(ods_code, vmp_snomed_code), index = year_month) %>% 
  fill_gaps(total_quantity = 0, .full = TRUE) %>% 
  tidyr::fill(.direction = "down") %>% 
  as_tibble() %>% 
  mutate(year_month = floor_date(year_month, unit = "month")) %>% 
  group_by(year_month, ods_code, vmp_snomed_code) %>% 
  arrange(ods_code, vmp_snomed_code, year_month) %>% 
  mutate(total_quantity = sum(total_quantity)) %>%
  arrange(ods_code, vmp_snomed_code, year_month) %>% 
  distinct() %>% 
  group_by(ods_code, vmp_snomed_code) %>%
  dplyr::summarise(count_sparkline = list(total_quantity)) %>% 
  group_by(ods_code, vmp_snomed_code) %>% 
  dplyr::mutate(total_quantity = sum(unlist(count_sparkline)))

# Create lookup datasets for joining
# SNOMED
vmp_snomed_names_lookup <- df_scmd_names %>% 
  select(vmp_snomed_code, vmp_product_name) %>% 
  dplyr::distinct()

# Trust
trust_names_lookup <- df_scmd_names %>% 
  select(ods_code, ods_name, stp_code) %>% 
  dplyr::distinct()

# Join data
df_tab_sparkline <- df_tab_sparkline %>%
  ungroup() %>% 
  arrange(ods_code) %>% 
  left_join(trust_names_lookup, by = c("ods_code")) %>% 
  left_join(vmp_snomed_names_lookup, by = c("vmp_snomed_code")) %>% 
  mutate(count_box = count_sparkline)

# See the ?tippy documentation to learn how to customize tooltips
with_tooltip <- function(value, tooltip, ...) {
  div(style = "text-decoration: underline; text-decoration-style: dotted; cursor: help",
      tippy(value, tooltip, ...))
}
# Create table
df_tab_sparkline %>% 
  select(ods_name, vmp_product_name,vmp_snomed_code, count_sparkline, count_box, total_quantity, 
         -ods_code, -stp_code) %>% 
  reactable(filterable = TRUE,
            defaultSorted = c("ods_name", "total_quantity"),
            groupBy = c("ods_name"),
            columns = list(
              ods_name = reactable::colDef(name = "Trust", 
                                           minWidth = 200),
              count_sparkline = colDef(name = "Trend",
                                       header = with_tooltip("Trend", "Note that the y axis cannot be compared across different entries."),
                                       minWidth = 50,
                                       cell = function(value, index) {
                                         sparkline(df_tab_sparkline$count_sparkline[[index]])
                                       }),
              count_box = reactable::colDef(show = FALSE),
              total_quantity = reactable::colDef(name = "Quantity",
                                                 minWidth = 50,
                                                 aggregate = "sum",
                                                 format = reactable::colFormat(digits = 0)),
              vmp_product_name = reactable::colDef(name = "Product", 
                                                   minWidth = 150,
                                                   cell = function(value, index) {
                                                     vmp_snomed_code <- paste0("SNOMED: ", df_tab_sparkline$vmp_snomed_code[index])
                                                     vmp_snomed_code <- if (!is.na(vmp_snomed_code)) vmp_snomed_code else "Unknown"
                                                     div(
                                                       div(style = list(fontWeight = 600), value),
                                                       div(style = list(fontSize = 10), vmp_snomed_code))
                                                   }
              ),
              vmp_snomed_code = reactable::colDef(show = FALSE)
            ),
            style = list(fontSize = "12px"),
            highlight = TRUE
)

dm+d

Information from the dm+d (Dictionary of Medicines and Devices) on the strength and vmp quantity of each asthma biologic at VMP level, using data hosted on the DataLab BigQuery server.

db_dmd_info <- dplyr::tbl(conn_ebm_scmd, sql_query_dmd_info)

df_dmd_info <- db_dmd_info %>% 
  filter(vmp_snomed_code %in% codes) %>% 
  collect()

We use information on the daily defined dose (DDD) of each asthma biologic, so that the volume of each VMP can be compared directly once converted to DDDs. The WHO publish DDDs online.

# Define tibble with mg_per_ddd for join later
ddds <- select(codelist, c('nm', 'ddd')) %>% 
        drop_na('ddd')

# get additional DDDs sourced elsewhere
add_ddds <- read_csv(here("data/meds_covid_meds_additional_ddds.csv"),
                    # convert to strings (they are stored as strings in SCMD table)
                    col_types = cols(id = col_character()))

add_ddds <- select(add_ddds, c('nm', 'ddd'))
ddds <- rbind(ddds, add_ddds)

df_scmd_mg <- df_scmd_names %>% 
  left_join(df_dmd_info, by = c("vmp_snomed_code", "vmp_product_name")) %>% 
  left_join(ddds, by = c("vmp_product_name" = "nm"))  
df_scmd_mg %>% 
  select(vmp_snomed_code, vtmnm, form_descr, udfs, udfs_descr,
         strnt_nmrtr_val, strnt_nmrtr_uom, strnt_dnmtr_val,strnt_dnmtr_descr, 
         ddd) %>% 
  distinct() %>% 
    reactable(filterable = TRUE,
              columns = list(
                vmp_snomed_code = reactable::colDef(name = "SNOMED", 
                                                    minWidth = 100),
                vtmnm = reactable::colDef(name = "Name", 
                                          minWidth = 100),
                form_descr = reactable::colDef(name = "Form", 
                                               minWidth = 80),
                # udfs is the VMP unit dose form strength
                udfs = reactable::colDef(name = "Value", 
                                         minWidth = 40),
                udfs_descr = reactable::colDef(name = "Unit", 
                                               minWidth = 40),
                # strnt_nmrtr
                strnt_nmrtr_val = reactable::colDef(name = "Numerator", 
                                                    minWidth = 60,
                                                    format = colFormat(suffix = " mg")),
                strnt_nmrtr_uom = reactable::colDef(show = FALSE),
                # strnt_dnmtr
                strnt_dnmtr_val = reactable::colDef(name = "Denominator", 
                                                    minWidth = 60,
                                                    format = colFormat(suffix = " ml")),
                strnt_dnmtr_descr = reactable::colDef(show = FALSE),
                ddd = reactable::colDef(name = "mg/ddd", 
                                               minWidth = 50)),
              columnGroups = list(
                colGroup(name = "UDFS", columns = c("udfs", "udfs_descr")),
                colGroup(name = "Strength", columns = c("strnt_nmrtr_val", "strnt_dnmtr_val"))
              ),
              style = list(fontSize = "12px"),
              highlight = TRUE)

Convert volume

The final data cleaning step is to convert the volume from VMP quantity (as provided in the SCMD dataset) to volume in DDDs.

  • SCMD volumes data is provided in vmp quantity - this means different things for different products. e.g. 100mg powder = 1 vmp quantity but 100mg/20ml solution for injection = 20 vmp quantity, even though both VMPS have the same strength of ingredient.
  • To translate volume in VMP quantity to volume in DDDs we need to go through a few steps:
    • First, translate volume in VMP quantity to volumes in singles of the product (i.e. number of vials)
    • Then translate volume in singles of product to volume in strength of ingredient (i.e. number of mgs of active ingredient)
    • Finally translate volume in strength of ingredient to volume in DDDs, using the DDD information published by the WHO
df_scmd_ddd <- df_scmd_mg %>% 
  mutate(volume_singles = total_quantity / udfs,
         volume_mg_strength = volume_singles * if_else(is.na(strnt_dnmtr_val), 
                                                       true = strnt_nmrtr_val, 
                                                       false = strnt_nmrtr_val * 
                                                               (udfs / strnt_dnmtr_val)),
         volume_ddd = volume_mg_strength / ddd)

Results

Total volume per year

Regional prescribing

temp_ggplot <- df_scmd_ddd_map %>% 
  filter(year_month >= as.Date("2021-04-01") & year_month < as.Date("2022-04-01")) %>%
  select(year_month, ods_code, vtmnm, volume_ddd, comm_region_name) %>% 
  mutate(comm_region_name = fct_explicit_na(comm_region_name)) %>% 
  group_by(comm_region_name, vtmnm) %>%
  summarise(volume_ddd = sum(volume_ddd, na.rm = TRUE)) %>%
  group_by(comm_region_name) %>%
  mutate(prop_use = volume_ddd / sum(volume_ddd, na.rm = TRUE),
         pos = cumsum(volume_ddd) - volume_ddd/2,
         total = sum(volume_ddd)) %>%
  ungroup() %>% 
  mutate(comm_region_name = fct_reorder(comm_region_name, total)) %>% 
  ggplot(aes(comm_region_name)) +
  geom_bar(aes(y = volume_ddd,
               fill = vtmnm,
               text = paste0("<b>Region:</b> ", comm_region_name, "<br>",
                             "<b>Total volume in ddd:</b> ", round(total, 0), "<br>",
                             # "<b>Medication:</b> ", vtmnm, "<br>",
                             "<b>", vtmnm , " volume in ddd (%):</b> ", round(volume_ddd, 0), " (", scales::percent(prop_use, accuracy = 0.1), ")"
                             )
               ), 
           stat='identity',
           # position = position_dodge()
           ) +
  scale_fill_viridis_d() +
  labs(subtitle = paste0("From: ", min(df_scmd_ddd_map$year_month), " to ", max(df_scmd_ddd_map$year_month)),
       x = NULL,
       y = "Defined Daily Dose",
       fill = NULL) +
  scale_y_continuous(labels = scales::comma) +
  theme(text = element_text(size = 12)) +
  coord_flip()

# temp_ggplot
plotly::ggplotly(temp_ggplot,
                 tooltip = "text") %>%
  plotly::config(displayModeBar = FALSE)

Figure. Total regional prescribing of COVID treatments by region for financial year 2021-22.

STPs

df_scmd_ddd_map_temp <- df_scmd_ddd_map %>% 
  filter(year_month >= as.Date("2021-04-01") & year_month < as.Date("2022-04-01")) %>%
  select(year_month, ods_code, vtmnm, volume_ddd, stp_name) %>% 
  mutate(stp_name = fct_explicit_na(stp_name)) %>% 
  group_by(stp_name, vtmnm) %>%
  summarise(volume_ddd = sum(volume_ddd)) %>%
  group_by(stp_name) %>%
  mutate(prop_use = volume_ddd / sum(volume_ddd, na.rm = TRUE),
         pos = cumsum(volume_ddd) - volume_ddd/2,
         total = sum(volume_ddd, na.rm = TRUE)) %>%
  ungroup() %>% 
  mutate(rank = dense_rank(-total),
         stp_name = fct_reorder(stp_name, -rank))

temp_ggplot <- df_scmd_ddd_map_temp %>% 
  filter(rank <= 20) %>% 
  ggplot(aes(stp_name)) +
  geom_bar(aes(y = volume_ddd,
               fill = vtmnm,
               text = paste0("<b>STP:</b> ", stp_name, "<br>",
                             "<b>Total volume in ddd:</b> ", round(total, 0), "<br>",
                             "<b>", vtmnm , " volume in ddd:</b> ", round(volume_ddd, 0), " (", scales::percent(prop_use, accuracy = 0.1), ")")),
           stat = 'identity') +
  scale_fill_viridis_d() +
  labs(subtitle = paste0("From: ", min(df_scmd_ddd_map$year_month), " to ", max(df_scmd_ddd_map$year_month)),
       x = NULL,
       y = "Defined Daily Dose",
       fill = NULL) +
  scale_y_continuous(labels = scales::comma) +
  theme(text = element_text(size = 12),
        legend.position = "bottom") +
  coord_flip()

# temp_ggplot
plotly::ggplotly(temp_ggplot,
                 tooltip = "text") %>%
  plotly::config(displayModeBar = FALSE) %>% 
layout(legend = list(orientation = "h", x = -0.5, y =-.15))

Figure. Total prescribing of COVID treatments in financial year 2021-22 for the top 20 STPs with the largest volume across all selected medications. 

# Create table, code here: "scripts/create_tables.R"
# the data is defined above and needs to contain the following columns:
# - stp_name <fct>
# - vtmnm <chr>
# - volume_ddd <dbl>
# - prop_use <dbl>
# - pos <dbl>
# - total <dbl>
# - rank <int>

create_med_use_table(data = df_scmd_ddd_map_temp, field = 'vtmnm')

References

Goldacre, Ben, and Brian MacKenna. 2020. “The NHS Deserves Better Use of Hospital Medicines Data.” BMJ 370 (July): m2607. https://doi.org/10.1136/bmj.m2607.